How to Build an Internal AI Agent That Evolves Itself

AnswerThis builds AI agents for evidence-based scientific workflows and has scaled past $2 million in ARR with just two full-time employees — largely because they built an internal AI ops agent that processes over 100 emails a day, closes support tickets, and updates their CRM automatically. In this recent batch talk, founder Ayush Garg breaks down the architecture of a self-extending agent that builds its own tools when it encounters tasks it can't handle yet, how his non-technical co-founder trains the agent by giving it feedback in Slack, and the three types of memory — factual, behavioral, and procedural — that any founder can copy to build an internal agent for their own business.

May 19, 20265mWatch on YouTube ↗

EPISODE INFO

Released: May 19, 2026
Duration: 5m
Channel: YC Root Access
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

AnswerThis builds AI agents for evidence-based scientific workflows and has scaled past $2 million in ARR with just two full-time employees — largely because they built an internal AI ops agent that processes over 100 emails a day, closes support tickets, and updates their CRM automatically. In this recent batch talk, founder Ayush Garg breaks down the architecture of a self-extending agent that builds its own tools when it encounters tasks it can't handle yet, how his non-technical co-founder trains the agent by giving it feedback in Slack, and the three types of memory — factual, behavioral, and procedural — that any founder can copy to build an internal agent for their own business.

EPISODE SUMMARY

In this episode of YC Root Access, How to Build an Internal AI Agent That Evolves Itself explores self-extending internal AI ops agent built with Claude Code CLIs AnswerThis uses an internal AI ops agent to offload founder work, processing 100+ emails/day, resolving 400+ support tickets, and updating CRM and feedback loops.

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